In this section, we assume that there are 1000 nodes in the network (i.e., 1000 agents in the entire system). One of the agents (let it be Agent 1) holds the technological lead in the new advanced technology, which means, it has already possessed some knowledge and experience of T3. Naturally, Agent 1 will adopt T3 earlier than the other agents and act as the leader in the network. Other 999 agents are thus the followers in the network. Each agent makes technology adoption decisions with the optimization model introduced in the previous section, and each agent also adopts the moving window limited foresight decision scheme with the same foresight of 50years, which follows previous literature33. The decision horizon is 100years. Other initial parameters for all the agents are the same (as shown in Table S1 in the Supplementary Methods), except that for Agent 1, (overline{x}_{3}^{0} = 50). The settings of the parameters initial values also follow previous researches29. The simulation settings are deliberately simplified so that we can observe more transparently how the new advanced technology diffuses from the leader to the followers in the network with different topological structures. It should be noted that, in our model, each technology essentially represents a cluster of related technologies with the infrastructure being in the center. The diffusion of a cluster of new technology can span up to a century . It takes even a longer time to evaluate the climate impact of a new technology. Therefore, we set the simulation time to 100years.
Figure1 shows how T3 is adopted by the leader (Agent 1) and the followers (other agents) individually in the reference scenario when all the agents make decisions independently without exchanging any information or knowledge with each other, i.e., theres no technological spillover among agents. Since the leader holds the technological lead in the new advanced technology T3, it adopts T3 around the year 2020, while the followers adopt T3 around the year 2080 (when the share of T3 reaches 10%). How the new advanced technology T3 is adopted in the entire system is also shown in Fig.1. Due to the fact that the followers take up the overwhelming majority number of all the agents, T3s adoption in the entire system seems the same as the followers, which is around the year 2080.
Reference scenario in Simulation. Lines in the figure represent how the shares of T3 change over time for the leader and the followers individually(top panel), and in the entire system(bottom panel).
The no-spillover scenario can also be viewed as the investment failure scenario, since the followers cannot acquire the knowledge of the new technology from the leader and continue to use the old technology until the new technology becomes cost-effective for them. In the following simulations, we assume that the technological spillover effect exists among the agents. We will investigate how T3 diffuses from the leader to the followers when the technological spillover network is a regular lattice, a random network, or a scale-free network, respectively.
In a globally coupled network, all nodes are connected with each other, as illustrated in the left panel of Fig.2, taking a network with 10 nodes as an example.
Illustration of three typical regular lattices.
Since all the 1000 agents in the network are connected with each other, every follower agent acquires the same information at each time t, and thus makes the same technology adoption decisions. Figure3 shows how T3 is adopted by the leader and the followers individually and in the entire system. Compared with the results in the reference scenario, T3 is adopted 30years earlier by the followers and in the entire system in the globally coupled network.
Adoption of T3 in the globally coupled network. Lines in the figure represent how the shares of T3 change over time.
Figure4 illustrates the agents in the network who adopt T3 at different times (as shown in Fig.4a), agents who do not adopt T3 are removed from the figures) and the number of agents that adopt T3 at different times (as shown in Fig.4b)). To make the description clearer, agents who adopt T3 at time t are named active agents at time t. The leader is highlighted with a star-shaped node, while the followers are represented with dot nodes. The edges among the nodes are shown with grey lines in the figures, which represent the connections among agents. As we can observe in Fig.4, in the early stage (20002010), no agent adopts T3; from the year 2020 to 2040, only the leader adopts T3; after the year 2050, all the followers start to adopt T3. This is because, in a globally coupled network, all the followers are connected to the leader and with each other. Therefore, they adopt the new advanced technology at the same time.
Active agents and the number of active agents in the globally coupled network. Sub-figure(a) presentsactive agents at different times, and sub-figure(b) plots thenumber of active agents at different times. In sub-figure (a), the star-shaped node represents the leader, the black dot nodes represent the followers, and the grey lines represent the edges that connect the agents.
In a nearest-neighbor coupled network, each node only connects with its K neighbor nodes (K/2 neighbor nodes on each side), as illustrated in the middle panel of Fig.2, taking 10 nodes and K=4 as an example.
In our simulation, we let K=100, which means each agent connects with its 100 neighbor agents (50 neighbor agents on each side). Figure5 shows how T3 is adopted by each individual agent and in the entire system. The followers adopt T3 gradually since they acquire the knowledge of the new advanced technology at different times. The earliest and the latest adoption of T3 for the followers are around the years 2050 and 2080. There is an adoption time lag of 30years among the followers. In the entire system, T3 is adopted around the year 2060, 20years earlier compared with the results in the reference scenario.
Adoption of T3 in the nearest-neighbor coupled network. Lines in the figure represent how the shares of T3 change over time.
Figure6 illustrates the active agents at different times (as shown in Fig.6a) and the number of active agents that changes over time (as shown in Fig.6b) in the nearest-neighbor coupled network. The star-shaped node also represents the leader, and the dot nodes also represent the followers. As we can observe, in 2050, not many followers adopt T3. After that, more and more followers start to adopt T3 and the number of active agents keeps increasing. After the year 2080, T3 is adopted by all the agents. This is because, in a nearest-neighbor coupled network, each agent connects with its neighbor nodes. Followers can only acquire the knowledge of T3 gradually from its neighbors. As a result, the number of active agents also increases gradually.
Active agents and the number of active agents in the nearest-neighbor coupled network. Sub-figure(a) presentsactive agents at different times, and sub-figure(b) plotsthe number of active agents at different times. In sub-figure (a), the star-shaped node represents the leader, the black dot nodes represent the followers, and the grey lines represent the edges that connect the agents.
In a star coupled network, there is one node that occupies the center of the star and all the other nodes only connect with the center node, as illustrated in the right panel of Fig.2, taking 10 nodes as an example.
In our simulation, Agent 1 has the technological lead, therefore, it is assumed intuitively to be the center node, and all the followers connect with and only with it. As a matter of fact, since all the followers are identical and only connect with the leader, they also acquire the same knowledge at each time t, and thereby make the same technology adoption decisions as well. Figure7 shows how T3 is adopted by the leader and the followers individually and in the entire system. Compared with the results in the reference scenario, T3 is adopted 30years earlier by the followers and in the entire system.
Adoption of T3 in the star coupled network. Lines in the figure represent how the shares of T3 change over time.
Figure8 illustrates the active agents at different times and the number of active agents that changes over time in the star coupled network. From the year 2020 to 2040, only the leader adopts T3. After the year 2050, all the followers start to adopt T3. This is because, in a star coupled network, the followers only connect to the leader and can acquire the knowledge of T3 directly from the leader. Therefore, similar to the globally coupled scenario, they adopt T3 quickly and at the same time.
Active agents and the number of active agents in the star coupled network. Sub-figure(a) presentsactive agents at different times, and sub-figure(b) plots thenumber of active agents at different times. In sub-figure (a), the star-shaped node represents the leader, the black dot nodes represent the followers, and the grey lines represent the edges that connect the agents.
In this subsection, we will run simulations under the assumption that the technological spillover network among all the 1000 agents is a random network. We follow the study conducted by Erds and Rnyi to construct the ER random graph (G_{N,p}^{ER}), where each pair of nodes is assumed to be connected with a probability p35. To capture its dynamics, we also assume that the static random networks among all the nodes are different at different times.
According to the random graph theory, suppose that there are N nodes in a network, there is a critical probability (p_{c} = 1/N). Also, when (p ge {text{ln}}left( N right)/N), almost any graph in the ensemble (G_{N,p}^{ER}) is totally connected35,36. Therefore, in our research, we will run different simulations with the connecting probability p equals to 0.001, 0.01, and 0.1, respectively. We will explore how the new advanced technology diffuses in the randomly connected networks in the following three scenarios: (a) when the connecting probability is comparatively low; (b) when there exists a giant component in the network; and (c) when the random network is always connected.
Figure9 shows how T3 is adopted by each individual agent (sub-figures (a), (b) and (c), in which the dashed line represents the leader and the solid lines represent the followers) and in the entire system (sub-figure (d)) when the connecting probability (p) varies. When (p = 0.001), the followers adopt T3 around the years 2060 to 2080. When (p = 0.1), the followers generally adopt T3 earlier, around the years 2050 to 2060. In the entire system, when p increases from the 0.001 to 0.1, the adoption of T3 is advanced from the year 2080 to the year 2060.
Adoption of T3 with different connecting probabilities. Lines in the figure represent how the shares of T3 change over time.
Figure10 shows the active agents at different times (here we present the active agents in 2050 and 2070 as illustrations) and how the number of active agents changes over time when (p) varies. Likewise, the star-shaped node represents the leader, the black dot nodes represent the followers, and the grey lines represent the edges that connect the agents. As we can observe, when (p = 0.001), in 2050, only the leader adopts T3; in 2070, only one follower starts to adopt T3. When (p = 0.01), in 2050, several followers adopt T3; in 2070, more followers adopt T3. When (p = 0.1), in 2050, about 90 followers start to adopt T3; in 2070, all followers adopt T3. Note that, in the figure, there are independent dot nodes (without the connection to any other nodes). This is because, in our simulation, the random network structure varies at each time t. Previous connected nodes may not be connected currently, but they have already acquired the knowledge of T3 through previous connections. From Fig.10b), we can conclude that, the higher the connecting probability is, the faster the number of active agents increases, that is, the earlier the followers adopt the new advanced technology T3.
Active agents and the number of active agents in the random networks. Sub-figure(a) presentsactive agents at different times,and sub-figure (b) plots thenumber of active agents at different times. In sub-figure (a), the star-shaped node represents the leader, the black dot nodes represent the followers, and the grey lines represent the edges that connect the agents.
In a word, as the connecting probability (p) increases, more and more followers tend to initiate their adoption of the new advanced technology earlier. It is in accordance with our intuition that with a larger connecting probability, more follower agents could be topologically connected with the leader agent, and they can benefit from the technological spillover effect more straightforwardly.
In this subsection, we will run simulations under the assumption that the technological spillover network among all 1000 agents is a BA scale-free network. The ER random graph neglects two most important features of the network in the real world: growth and preferential attachment37.
Following the approach proposed by Albert and Barabsi, we construct the scale-free network with the following rules38:
Initially, there exists a connected network with (m_{0}) nodes. We assume that there are (m_{0} = 10) nodes connecting with each other randomly in the initial network, including the leader Agent 1. The remaining 990 nodes are pending to be added to the network. In each iteration, we add a new node with m edges that link the new node to m present nodes in the system. m should be less than (m_{0}), and we let m=5.
When choosing the nodes to which the new node connects, the probability of the new node connecting with a present node i in each iteration is computed with the following equation:
$$begin{array}{*{20}c} {p_{i} = frac{{k_{i} }}{{mathop sum nolimits_{j} k_{j} }},} \ end{array}$$
(8)
where, (k_{i}) is the degree of present node i, (mathop sum limits_{j} k_{j}) represents the sum of the degrees of all present nodes. Figure11 shows the power-law degree distribution of the BA scale-free network we constructed.
The power-law degree distribution of the constructed scale-free network.
Figure12 presents how T3 is adopted by individual agents and in the entire system. As we can observe, the followers adoption time of T3 ranges from the year 2050 to the year 2080. In the entire system, T3 is adopted around the year 2070, only 10years earlier compared with what suggested in the reference scenario.
Adoption of T3 in the scale-free network. Lines in the figure represent how the shares of T3 change over time.
Figure13 illustrates the active agents at different times and how the number of active agents changes over time in a scale-free network. As we can see, from the year 2020 to 2040, only the leader adopts T3; from the year 2050 to 2070, more and more followers start to adopt T3; and after the year 2080, all the followers adopt T3. This is because, in the scale-free network, most edges are connected to some nodes, i.e., a large number of agents are connected to only some agents, while the other agents are only connected to few agents. Those followers that link directly with the leader will adopt T3 earlier than the followers that are moredistant from the leader.
Active agents and the number of active agents in the scale-free network. Sub-figure(a) presentsactive agents at different times, and sub-figure(b) plots thenumber of active agents at different times. In sub-figure (a), the star-shaped node represents the leader, the black dot nodes represent the followers, and the grey lines represent the edges that connect the agents.
With the discussion above, we can draw the conclusion that, the topological structure of the technological spillover network among agents plays a crucial part in the adoption and diffusion of the new advanced technology among agents and in the entire system. Some network topological structures favor the adoption and diffusion of the new technology (e.g., the globally coupled network), while others may cause adoption time lags among the follower agents (e.g., the random network and the scale-free network), and therefore delay the adoption of the new technology in the entire system. The implication of our simulation is that, in the real world, the network topological structure among decision entities might be complex. It is crucial for the social planner to take into consideration of the network structure when evaluating the diffusion process of an innovation.
In the next section, we will discuss the implication of our research further. We will examine whether different network topological structures influence the effectiveness of a carbon emission constraint policy.
See the rest here:
- Report: Apple acquires French startup behind AI and computer vision technology - 9to5Mac - April 22nd, 2024 [April 22nd, 2024]
- CACI Awarded $1.3 Billion Task Order to Provide Communications and Information Technology Expertise to U.S. ... - Business Wire - April 22nd, 2024 [April 22nd, 2024]
- What is semi-automated offside technology and how does it work? - The Athletic - April 22nd, 2024 [April 22nd, 2024]
- Can technology save us from an ecological apocalypse? - interview - CyberNews.com - April 22nd, 2024 [April 22nd, 2024]
- Does LaLiga have goalline technology? What about other major leagues? - AS USA - April 22nd, 2024 [April 22nd, 2024]
- Driver Assistance Technologies: NHTSA Should Take Action to Enhance Consumer Understanding of Capabilities and ... - Government Accountability Office - March 31st, 2024 [March 31st, 2024]
- OpenAI reveals Voice Engine, but won't yet publicly release the risky AI voice-cloning technology - The Associated Press - March 31st, 2024 [March 31st, 2024]
- Nexalin Technology Full Year 2023 Earnings: US$0.63 loss per share (vs US$0.30 loss in FY 2022) - Yahoo Finance - March 31st, 2024 [March 31st, 2024]
- 'Battle for your brain': What the rise of brain-computer interface technology means for you - WBUR News - March 31st, 2024 [March 31st, 2024]
- Firsthand Technology Value Fund (NASDAQ:SVVC) Research Coverage Started at StockNews.com - Defense World - March 31st, 2024 [March 31st, 2024]
- Suzhou Anjie Technology Full Year 2023 Earnings: Misses Expectations - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Inside the shadowy global battle to tame the world's most dangerous technology - POLITICO Europe - March 31st, 2024 [March 31st, 2024]
- The Technological Pivot Of History: Power In The Age Of Exponential Innovation Analysis - Eurasia Review - March 31st, 2024 [March 31st, 2024]
- 'Women Behind the Wheel' explores the intersection of gender, culture and cars - NPR - March 31st, 2024 [March 31st, 2024]
- Shanghai Weihong Electronic Technology Full Year 2023 Earnings: Revenues Beat Expectations, EPS Lags - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- A Look At The Fair Value Of Powertech Technology Inc. (TWSE:6239) - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Weaver Network Technology Full Year 2023 Earnings: EPS Beats Expectations, Revenues Lag - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- New York City will introduce controversial AI gun detection technology amid subway crime crisis - SiliconANGLE News - March 31st, 2024 [March 31st, 2024]
- Earnings Not Telling The Story For Beijing CTJ Information Technology Co., Ltd. (SZSE:301153) - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Vontron Technology Full Year 2023 Earnings: EPS: CN0.35 (vs CN0.34 in FY 2022) - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Huawei Revenue Rises as Technology Giant Commits to Growth - Technology Magazine - March 31st, 2024 [March 31st, 2024]
- Shenzhen Fortune Trend technology Full Year 2023 Earnings: Beats Expectations - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- USPACE Technology Group Limited (HKG:1725) May Have Run Too Fast Too Soon With Recent 28% Price Plummet - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- NYC to test gun-detecting technology in subway system - SILive.com - March 31st, 2024 [March 31st, 2024]
- Cancer Treatment: 3D Printing and Scanning Technology - Surviving Mesothelioma - March 31st, 2024 [March 31st, 2024]
- Does Contel Technology (HKG:1912) Have A Healthy Balance Sheet? - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Oppo Find X7 series now supports 5G-Advanced technology with up to 10 Gbps downlink speed - The Indian Express - March 31st, 2024 [March 31st, 2024]
- DCPS receives nearly $20k in grants for technology program advancements - The Owensboro Times - March 31st, 2024 [March 31st, 2024]
- Hangzhou Electronic Soul Network Technology Full Year 2023 Earnings: Misses Expectations - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- International Business Digital Technology Full Year 2023 Earnings: CN0.07 loss per share (vs CN0.019 loss in FY ... - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Analysts Are More Bearish On Guangzhou Tinci Materials Technology Co., Ltd. (SZSE:002709) Than They Used To Be - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Daheng New Epoch Technology Full Year 2023 Earnings: EPS: CN0.11 (vs CN0.16 in FY 2022) - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Tesla offers U.S. customers a month's trial of its driver-assist technology - Reuters - March 31st, 2024 [March 31st, 2024]
- Slam Dunk Technology: How AI Is Revolutionizing The Game Of Basketball - Forbes - March 31st, 2024 [March 31st, 2024]
- China Environmental Technology and Bioenergy Holdings Full Year 2023 Earnings: CN0.03 loss per share (vs CN ... - Simply Wall St - March 31st, 2024 [March 31st, 2024]
- Vuma and Huawei team up to launch industry first 50G PON ... - Light Reading - July 15th, 2023 [July 15th, 2023]
- See What Kim Kardashian and Kylie Jenner Look Like With Aging ... - E! NEWS - July 15th, 2023 [July 15th, 2023]
- AFRL conducts swarm technology demonstration > ONE AFRL ... - afrl.af.mil - May 20th, 2023 [May 20th, 2023]
- Shell to use new AI technology in deep sea oil exploration - Reuters - May 20th, 2023 [May 20th, 2023]
- Former Google CEO says AI at 'center' of technology competition between US and China - Fox News - May 20th, 2023 [May 20th, 2023]
- Agriculture and technology combine to drive the industrys growth - Times of India - May 20th, 2023 [May 20th, 2023]
- Technology and the Skills Shortage - Financial Times - May 20th, 2023 [May 20th, 2023]
- New License Agreement Announced for Next-Generation Base ... - BioPharm International - May 20th, 2023 [May 20th, 2023]
- Orion Governance Licenses Technology from GE to Deliver Next Generation Data Governance Solution - Yahoo Finance - May 20th, 2023 [May 20th, 2023]
- World needs to be 'vigilant' as AI technology improves and ... - KTVZ - May 20th, 2023 [May 20th, 2023]
- After Losing Son, Ridgefield Mother Pushes For Technology to Prevent Hot Car Deaths - NBC Connecticut - May 20th, 2023 [May 20th, 2023]
- After last year's fan violence in Queretaro, has Fan ID technology ensured safety for Liga MX fans? - ESPN - ESPN - May 20th, 2023 [May 20th, 2023]
- Bleach: The Soul Reapers' Gigai Technology, Explained - CBR - Comic Book Resources - May 20th, 2023 [May 20th, 2023]
- Cogito Tech - Catalyzing Transformation in Global Healthcare ... - Business Standard - May 20th, 2023 [May 20th, 2023]
- Barriers to Use of Technology in Diabetes Management - Patient Care Online - May 20th, 2023 [May 20th, 2023]
- Blue technology startups presented at the inaugural Gulf Blue ... - The University of Southern Mississippi - May 20th, 2023 [May 20th, 2023]
- CureVac files expanded patent lawsuit against Pfizer/BioNTech over ... - Reuters - May 20th, 2023 [May 20th, 2023]
- Harrison Ford defends use of de-ageing technology in new Indiana Jones film: I know that that is my face - Yahoo News - May 20th, 2023 [May 20th, 2023]
- Sanwo-Olu: Nigeria needs technology to compete with likes of China - Guardian Nigeria - May 20th, 2023 [May 20th, 2023]
- Prejudice in technology, and the necessity of time: Books in brief - Nature.com - May 20th, 2023 [May 20th, 2023]
- New technology uses ordinary sunlight to disinfect drinking water ... - Stanford University News - May 20th, 2023 [May 20th, 2023]
- Incredible AI technology shows what UK cities will look like in 2050 - LADbible - May 20th, 2023 [May 20th, 2023]
- Your Firm and Your Tools - Top Technology Initiatives - CPAPracticeAdvisor.com - May 20th, 2023 [May 20th, 2023]
- Tom Hanks: I could appear in movies after death with AI technology - BBC - May 20th, 2023 [May 20th, 2023]
- Transform your career with Chief Technology Officer online course - Economic Times - May 20th, 2023 [May 20th, 2023]
- e-Learning Jamaica Technology in Education Conference Slated for ... - Government of Jamaica, Jamaica Information Service - May 20th, 2023 [May 20th, 2023]
- Andrew Maynard | What's a Luddite? From Industrial Revolution to ... - TribDem.com - May 20th, 2023 [May 20th, 2023]
- At Yale, Kaloyan Kolev used technology to create and to make ... - Yale News - May 20th, 2023 [May 20th, 2023]
- This technology could alter the entire planet. These groups want every nation to have a say. - MIT Technology Review - April 17th, 2023 [April 17th, 2023]
- The secret lives of snakes and how Georgia College uses technology to study them - 13WMAZ.com - April 8th, 2023 [April 8th, 2023]
- Technology Innovation Institute to host 2nd 'Additive Manufacturing the Future' seminar in Abu Dhabi - Devdiscourse - April 8th, 2023 [April 8th, 2023]
- Can Array Technologies Inc (ARRY) Stock Rise to the Top of Technology Sector Thursday? - InvestorsObserver - March 31st, 2023 [March 31st, 2023]
- Here's Why We Think Pfeiffer Vacuum Technology (ETR:PFV) Might Deserve Your Attention Today - Simply Wall St - February 18th, 2023 [February 18th, 2023]
- Will WM Technology Inc (MAPS) Stay at the Top of the Technology Sector? - InvestorsObserver - February 18th, 2023 [February 18th, 2023]
- Ways in which technology can enhance the abilities of law enforcement agents to assist the community - Times of India - February 7th, 2023 [February 7th, 2023]
- Meet The Titans: Google And OpenView (Microsoft) Faceoff On Chat Technology Innovation - Forbes - February 5th, 2023 [February 5th, 2023]
- MACOM Technology Solutions Holdings, Inc.'s (NASDAQ:MTSI) Stock Has Been Sliding But Fundamentals Look Strong: Is The Market Wrong? - Simply Wall St - February 5th, 2023 [February 5th, 2023]
- WVU Dept. of Ophthalmology acquires state-of-the-art technology for simulation lab - WV News - February 5th, 2023 [February 5th, 2023]
- Executive Vice President of Technology & Operations Alok Sethi Just Sold A Bunch Of Shares In Franklin Resources, Inc. (NYSE:BEN) - Simply Wall St - February 5th, 2023 [February 5th, 2023]
- Is Now The Time To Put Amkor Technology (NASDAQ:AMKR) On Your Watchlist? - Simply Wall St - January 27th, 2023 [January 27th, 2023]
- There Are Reasons To Feel Uneasy About New Oriental Education & Technology Group's (NYSE:EDU) Returns On Capital - Simply Wall St - January 27th, 2023 [January 27th, 2023]
- Technology has set us on a path toward one of two dystopian scenariosbut its not too late to save democracy - Fortune - January 17th, 2023 [January 17th, 2023]
- IonQ Acquires Entangled Networks And Locks In Quantum Networking Technology Critical To Its Future Success - Forbes - January 10th, 2023 [January 10th, 2023]
- Industrialization 3.0 - How Technology, Wall Street, And The Government Can Help The US Win In A World Of Re-Industrialization - Forbes - January 10th, 2023 [January 10th, 2023]
- Connect with aspirational India through technology and work in interest of world: Anurag Thakur to NRIs - Economic Times - January 8th, 2023 [January 8th, 2023]